by Gino Terentim a Management 3.0 Facilitator
This is part one of a two part blog, stay tuned for part two coming soon
A few weeks ago I wondered how to start this article when I received a WhatsApp message saying that I had to talk about complexity. There was an episode in Honduras, where a bridge was built on the Choluteca River, and used to cross the river. Until then, everything was fine, except that a storm occurred and the river itself moved. The bridge is now on dry land, not being used for its intended purpose. The river is no longer there.
It was a perfect solution to the wrong problem.
This, for me, is one of the best examples of how an “obvious” solution is totally ineffective when it comes to complexity. The solution is perfect, as long as the problem does not change over time.
Reflecting on this, I asked myself:
How many bridges, like Rio Choluteca’s are spread across our companies consuming resources and making organizations increasingly heavy because they have to carry the price of rework and waste?
The truth is that, as time goes on, the cycle of change becomes shorter and the pace at which new challenges are presented to organizations increases. I remember an article I read in Time magazine, called: The Year Man Becomes Immortal, about the accelerating pace of change and exponential growth in computing power that will lead to the Singularity.
While it took us 8000 years between the agricultural revolution and the industrial revolution, only nine years passed between the popularization of the internet and the sequencing of the human genome.
- How much time do our organizations have today to fully understand and understand a new problem before reacting?
- How much time do we have to gain enough knowledge about something so that we can establish a clear cause and effect relationship between a problem and a solution?
Decision-making in organizations is strongly based on the premise of repetition, predictability of the simplification of environments and reducing the whole to the sum of its parts.
Management 3.0, discusses that each element in a system is ignorant of the behavior of the system as a whole, and responds only to information that is available to it. This point is vitally important. If each element ‘knew’ what was happening to the system as a whole, all of the complexity would have to be present in that element.
In a school of fish, imagine if one was responsible for coordinating all of the movements and commanding the direction of the other fish and controlling whether everyone was executing as planned. He or she would most likely only notice the shark once several other fish had been eaten, because from his position he has only a partial view of the system and is unable to predict future behavior. The result can only be explained after it happens. Knowledge of past patterns is not enough (and may even be dangerous) to determine an answer in the future.
In a complex system, no one knows everything. Everyone only has one vision of the whole, but it is incomplete.
These two cases show the difficulty in using knowledge of the past to answer complex problems and ways of thinking. But, how do you know what is the best solution to a problem?
Her, we will look at two models for assessing complexity and at the end, a third (still incomplete and under construction), comparing them and highlighting their specific points.
I’ll start with the Cynefin Framework, created by Dave Snowden, a former senior consultant at IBM, when he was director of the Institute for Knowledge Management around the early 2000s. During this period, he led the team that developed Cynefin, a framework for decision making.
The Cynefin framework classifies problems into three types of systems:
The three systems are subdivided into five domains: Clear, Complicated, Complex and Chaotic, those four require leaders to diagnose situations and then act with the appropriate response for the context.
The fifth domain, disorder, applies when there is no clarity as to which of the other domains is predominant. Usually, something in disorder is influenced by more than one domain. The complicated and clear domains are part of the same ordered system, emphasizing that the boundary between both is human and non-systemic, that is, the agent’s level of understanding and knowledge will determine whether something is complicated or clear.
#1: Clear Domain: Best Practices
It is characterized by the existence of a clear cause and effect relationship. The answer is known to all and unquestionable. In this domain, it is up to the leaders and managers to sense, categorize — according to a base or catalog of best practice — and respond. In this context, the effect will always be known and predictable.
Here the focus is on efficiency and management practices retain the fundamental characteristics of command and control, with top-down decisions, clear and very well-defined processes, with high predictability. The Clear domain has little ambiguity and therefore decisions can be easily delegated, and functions are automated. The network is less important than the hierarchy
#2: Complicated Domain: Good Practices
This is the domain of analysis and experts. Here, unlike what occurs in the clear domain, several solutions are possible for the same problem and although there is also a clear cause and effect relationship, it is not so obvious to the point that anyone can interpret it and react by simply categorizing it.
You need to analyze the data. Here, the presence of a specialist is necessary so that he/she can make an analysis in order to select which practice may be the best practice for a given situation. The leader or manager, in a complicated context, must sense, analyze and then respond. While the clear domain does not benefit from the use of networks, in the complicated domain, networks gain strategic importance, since the opinions of experts will be necessary to determine the best solution.
#3: Complex Domain: Emerging Practices
While a complicated context has at least one right answer, in a complex context this cause-effect relationship cannot be established. In this domain, the understanding needed in order to solve a problem does not come from the past, but from the future. While the previous domains establish rules and standards for making the environment fail-safe, here the role of the leader or manager will be to create a safe environment in which to fail. An environment for experimenting and the search for an emerging pattern. This is the domain of experimentation, the development of hypotheses, tests and the search for feedback to improve solutions. Emerging patterns can be perceived, but they cannot be predicted. In this context for decision making, a leader must establish a safe environment so that, through probing, he can recognize emerging patterns and get rid of those he does not want.
#4: Chaotic Domain: Novel Practices
The domain of quick responses: In a chaotic context, the search for patterns, good practices and the right answers is useless, since the relationship between cause and effect is impossible to establish. There are no patterns, and when we think that a new pattern has been identified, it will change. When a problem presents itself in the chaotic domain, the leader or manager should not waste time looking for patterns. You must act as soon as possible to try and stabilize the dramatic situation, then sense if the situation is in fact under control and only then respond by adding predictability (and reducing uncertainty) to the situation so that it can move to another domain.